We evaluate our methods on two FSC benchmark datasets, i.e., mini-ImageNet [1]}, tiered-ImageNet [2]}. Both of them are the subset of ImageNet dataset [3]}.
For the mini-ImageNet, it consists of 100 classes and each class has 600 images with the size of \(84 \times 84\) . In experiments, we split the datasets as [4]}, and use 64, 16, 20 classes as the base set, validation set, novel set for mini-ImageNet, respectively.
For the tiered-ImageNet, it has 608 classes and each class contains \(1,281\)  images on average. Same as mini-ImageNet, the images are resized to \(84 \times 84\) .
In the experiment, 351, 97, and 160 classes are selected as the base set, validation set, and novel set, respectively.
Besides, researchers grouped the tiered-ImageNet dataset into 34 high-level categories, and each one has unbalanced classes, and the base, validation, and novel set come from different high-level categories.
